A Methodology For Large-Scale Identification of Related Accounts in Underground Forums

Cabrero-Holgueras, José; Pastrana, Sergio

doi:10.1016/j.cose.2021.102489

Cited by 6 publications

(1 citation statement)

References 37 publications

(85 reference statements)

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“…Using the CrimeBB dataset, the authors investigated the supply and demand for cybercrime services to the related community. Further, Cabrero-Holgueras & Pastrana [28] focused on the challenge of disparate user identities across different dark web forums to aid criminal investigations.…”

Section: Related Workmentioning

confidence: 99%

Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Deguara

Arshad

Paracha

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Cyber threats continue to grow with novel methods to attack computing systems, highlighting the need for sophisticated mechanisms and techniques to protect against such dynamic threats. Contemporary cyber defence mechanisms utilise a range of methods which rely on monitoring network or system-level events. However, with the growing use of the dark web by mal-actors to share exploits, breaches, and data leaks, the use of such information to strengthen defence mechanisms becomes an intriguing prospect. In this paper, we present our efforts to develop a text mining engine (Threat Miner) which analyses data from dark web forums and transforms it into actionable intelligence. Leveraging cutting-edge machine learning techniques and utilising a bespoke threat dictionary, Threat Miner extracts useful information from dark web forums into STIX form, enabling it to be used with threat intelligence platforms. We also present the results of a thorough evaluation of our scheme which was conducted with the CrimeBB dataset [1] to understand the feasibility of the approach as well as its effectiveness in strengthening defence capability against cyber threats.

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Section: Related Workmentioning

confidence: 99%

Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Deguara

Arshad

Paracha

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Applications of artificial intelligence to identify fake accounts: Psychological and legal aspects

Smolenskiy,

Levshin

2024

BIO Web Conf.

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This article analyzes the threats posed by the proliferation of fake accounts in today's information society. The authors consider the history of the emergence and development of this problem, as well as the ways proposed by governments and the international scientific community to address it. In search of a viable solution, they compare various methods for limiting the spread of fake accounts. They suggest using artificial intelligence technology as a promising alternative. As a training basis for AI, the authors propose analyzing the behavioral motivations of those who create fake accounts, in order to utilize a trained behavioral analytics system to combat fakes automatically. Along with key principles for using behavioral metrics, the authors provide various examples that illustrate the feasibility and effectiveness of their proposed solution. Special attention is paid to the legal aspects of implementing the proposed solution, as the process of detecting fake accounts is intricately linked to the processing of biometric and other personal data. The requirements for the storage and processing of personal data are becoming stricter every year. Therefore, in order to limit the spread of fake accounts, it is essential to find a solution that complies with information legislation.

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Building and Testing a Network of Social Trust in an Underground Forum: Robust Connections and Overlapping Criminal Domains

Manatova¹,

Sharma²,

Samtani

et al. 2022

2022 APWG Symposium on Electronic Crime Research (eCrime)

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A Methodology For Large-Scale Identification of Related Accounts in Underground Forums

Cited by 6 publications

References 37 publications

Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Applications of artificial intelligence to identify fake accounts: Psychological and legal aspects

Building and Testing a Network of Social Trust in an Underground Forum: Robust Connections and Overlapping Criminal Domains

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